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KODAMA (version 0.0.1)

Knowledge discovery by accuracy maximization

Description

KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.

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Version

Install

install.packages('KODAMA')

Monthly Downloads

748

Version

0.0.1

License

GPL (>= 2)

Maintainer

Stefano Cacciatore

Last Published

November 25th, 2014

Functions in KODAMA (0.0.1)

normalization

Normalization methods
MetRef

Nuclear Magnetic Resonance Spectra of Urines
classprob

Determines the Prevalence of Each Class
PLS.SVM.CV

Cross-Validation with Support Vector Machine.
core

Maximization of Cross-Validateed Accuracy Methods
swissroll

Swiss Roll Data Set Generator
majority

Determines Majority Class
KODAMA

Knowledge Discovery by Accuracy Maximization
kfold

k-Fold Partitioning
scaling

Scaling methods
USA

State of the Union Data Set
helicoid

Helicoid Data Set Generator
PCA.CA.KNN.CV

Cross-Validation with PCA-CA-kNN.
knn.predict

KNN Prediction Routine using Pre-Calculated Distances
knn.dist

Calculates the Distances for KNN Predictions
transformy

Conversion Classification Vector to Matrix
dinisurface

Ulisse Dini Data Set Generator
lymphoma

Lymphoma Gene Expression Dataset
knn.probability

KNN Prediction Probability Routine using Pre-Calculated Distances
KNN.CV

Cross-Validation with k-Nearest Neighbors Classifier.
spirals

Spirals Data Set Generator